A Swarm-based Data Sanitization Algorithm in Privacy-Preserving Data Mining

被引：0

作者：

Ming-Tai, Jimmy ^{[1
]}

Lin, Jerry Chun-Wei ^{[2
]}

Djenouri, Youcef ^{[3
]}

Fournier-Viger, Philippe ^{[4
]}

Zhang, Yuyu ^{[4
]}

机构：

[1] Shandong Univ Sci & Technol, Qingdao, Shandong, Peoples R China

[2] Western Norway Univ Appl Sci, Bergen, Norway

[3] Norwegian Univ Sci & Technol, Trondheim, Norway

[4] Harbin Inst Technol Shenzhen, Shenzhen, Guangdong, Peoples R China

来源：

2019 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) | 2019年

关键词：

Privacy-preserving data mining; multi-objective; PSO; Pareto solutions; NOISE;

D O I：

10.1109/cec.2019.8790271

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In recent decades, data protection (PPDM), which not only hides information, but also provides information that is useful to make decisions, has become a critical concern. We present a sanitization algorithm with the consideration of four side effects based on multi-objective PSO and hierarchical clustering methods to find optimized solutions for PPDM. Experiments showed that compared to existing approaches, the designed sanitization algorithm based on the hierarchical clustering method achieves satisfactory performance in terms of hiding failure, missing cost, and artificial cost.

引用

页码：1461 / 1467

页数：7

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